import torch
import torch.nn as nn
from torch.utils.data import DataLoader

import torchvision.datasets as DataSets
import torchvision.transforms


training_data = DataSets.FashionMNIST(
    root="./data",
    train=True,
    download=True,
    transform=torchvision.transforms.ToTensor()
)
test_data = DataSets.FashionMNIST(
    root="./data",
    train=False,
    download=True,
    transform=torchvision.transforms.ToTensor()
)

batch_size = 64
train_dataloader = DataLoader(training_data, batch_size=batch_size, drop_last = True)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

device = (
    "cuda"
    if torch.cuda.is_available()
    else "cpu"
)
print(f"Using {device} device")
net = nn.Sequential(
    nn.Flatten(),
    nn.Linear(28*28, 512),
    nn.ReLU(),
    nn.Linear(512, 512),
    nn.ReLU(),
    nn.Linear(512, 10),
).to(device)

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-3)

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    model.train()
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to(device), y.to(device)

        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        if batch % 100 == 0:
            loss, current = loss.item(), (batch + 1) * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(train_dataloader, net, loss_fn, optimizer)
    test(test_dataloader, net, loss_fn)
print("Done!")

classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

net.eval()
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
    x = x.to(device)
    pred = net(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')
